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Osman AFI, Tamam NM, Yousif YAM. A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck. J Appl Clin Med Phys 2023; 24:e14015. [PMID: 37138549 PMCID: PMC10476994 DOI: 10.1002/acm2.14015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/05/2023] Open
Abstract
PURPOSE In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. METHODS A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. RESULTS The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net. CONCLUSION All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.
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Affiliation(s)
| | - Nissren M. Tamam
- Department of PhysicsCollege of SciencePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia
| | - Yousif A. M. Yousif
- Department of Radiation OncologyNorth West Cancer Centre – Tamworth HospitalTamworthAustralia
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Schmidt MC, Abraham CD, Huang J, Robinson CG, Hugo G, Knutson NC, Sun B, Raranje C, Sajo E, Zygmanski P, Jandel M, Szentivanyi P, Hilliard J, Hamilton J, Reynoso FJ. Clinical application of a template-guided automated planning routine. J Appl Clin Med Phys 2023; 24:e13837. [PMID: 36347220 PMCID: PMC10018666 DOI: 10.1002/acm2.13837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/06/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application. METHODS A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan. RESULTS The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″. CONCLUSIONS Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.
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Affiliation(s)
- Matthew C Schmidt
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA.,Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Christopher D Abraham
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jiayi Huang
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Clifford G Robinson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Geoffrey Hugo
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Nels C Knutson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Baozhou Sun
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Chipo Raranje
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Erno Sajo
- Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Piotr Zygmanski
- Brigham and Women's/Dana Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts, USA
| | - Marian Jandel
- Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | | | - Jessica Hilliard
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jessica Hamilton
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Francisco J Reynoso
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
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Osman AFI, Tamam NM. Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer. J Appl Clin Med Phys 2022; 23:e13630. [PMID: 35533234 PMCID: PMC9278691 DOI: 10.1002/acm2.13630] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/20/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Deep learning-based knowledge-based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high-quality plans. This paper presents a novel KBP model using an attention-gating mechanism and a three-dimensional (3D) U-Net for intensity-modulated radiation therapy (IMRT) 3D dose distribution prediction in head-and-neck cancer. METHODS A total of 340 head-and-neck cancer plans, representing the OpenKBP-2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention-gated 3D U-Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean-squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U-Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground-truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning-based dose prediction models. RESULTS The proposed attention-gated 3D U-Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground-truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U-Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U-Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U-Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U-Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U-Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U-Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U-Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U-Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U-Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the D m a x ${D_{max}}$ (brainstem, spinal cord, and mandible) and D m e a n ${D_{mean}}$ (left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground-truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground-truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real-time applications. CONCLUSIONS The attention-gated 3D U-Net model demonstrated a capability in predicting accurate 3D dose distributions for head-and-neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U-Net model, and it was also competitive to the performance of the best state-of-the-art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision-making before planning, quality assurance of planning, and guiding-automated planning for improved plan consistency, quality, and planning efficiency.
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Affiliation(s)
| | - Nissren M Tamam
- Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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Fu Y, Zhang H, Morris ED, Glide-Hurst CK, Pai S, Traverso A, Wee L, Hadzic I, Lønne PI, Shen C, Liu T, Yang X. Artificial Intelligence in Radiation Therapy. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:158-181. [PMID: 35992632 PMCID: PMC9385128 DOI: 10.1109/trpms.2021.3107454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Hao Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Eric D. Morris
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Carri K. Glide-Hurst
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Suraj Pai
- Maastricht University Medical Centre, Netherlands
| | | | - Leonard Wee
- Maastricht University Medical Centre, Netherlands
| | | | - Per-Ivar Lønne
- Department of Medical Physics, Oslo University Hospital, PO Box 4953 Nydalen, 0424 Oslo, Norway
| | - Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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5
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Schmidt MC, Pryser EA, Baumann BC, Yaqoub MM, Raman CA, Szentivanyi P, Michalski JM, Gay HA, Knutson NC, Hugo G, Sajo E, Zygmanski P, Mazur T, Dise J, Cammin J, Laugeman E, Reynoso FJ. Development and Implementation of an Open Source Template Interpretation Class Library for Automated Treatment Planning. Pract Radiat Oncol 2021; 12:e153-e160. [PMID: 34839048 DOI: 10.1016/j.prro.2021.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/31/2021] [Accepted: 11/03/2021] [Indexed: 11/18/2022]
Abstract
PURPOSE Widespread implementation of automated treatment planning in radiation therapy remains elusive due to variability in clinic and physician preferences making it difficult to ensure consistent plan parameters. We have developed an open-source class library with the aim to improve efficiency and consistency for automated treatment planning in radiation therapy. METHODS AND MATERIALS An open source class library has been developed that interprets clinical templates within a commercial treatment planning system into a treatment plan for automated planning. This code was leveraged for the automated planning of 39 patients and retrospectively compared to the 78 clinically approved manual plans. RESULTS From the initial 39 patients, 74 of 78 plans were successfully generated without manual intervention. Target dose was more homogenous for automated plans, with an average homogeneity index of 3.30 vs 3.11 for manual and automated plans, respectively (p = 0.107). Generalized equivalent uniform dose decreased in the femurs and rectum for automated plans, with mean gEUD of 3746 cGy vs 3338 cGy (p ≤ 0.001) and 5761 cGy vs 5634 cGy (p ≤ 0.001) for femurs and rectum, respectively. Dose metrics for bladder and rectum (V6500 cGy and V4000 cGy) show recognizable but insignificant improvements. All automated plans delivered for quality assurance passed a gamma analysis (>95%) with an average composite pass rate of 99.3% and 98.8% for pelvis and prostate plans, respectively. Deliverability parameters such as total monitor units and aperture complexity indicate deliverable plans. CONCLUSIONS Prostate cancer and pelvic node radiotherapy can be automated using VMAT planning and clinical templates based on a standardized clinical workflow. The class library developed in this study conveniently interfaces between the plan template and the treatment planning system to automatically generate high quality plans on customizable templates.
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Affiliation(s)
- Matthew C Schmidt
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri; Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts.
| | - Eleanor A Pryser
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Brian C Baumann
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Mahmoud M Yaqoub
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Caleb A Raman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Hiram A Gay
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Nels C Knutson
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Geoffrey Hugo
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Erno Sajo
- Department of Physics, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Piotr Zygmanski
- Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Thomas Mazur
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Joseph Dise
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Jochen Cammin
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric Laugeman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
| | - Francisco J Reynoso
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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Peng J, Chen Y, Zhao J, Wang J, Xia X, Cai B, Mazur TR, Zhu J, Zhang Z, Hu W. An atlas-guided automatic planning approach for rectal cancer intensity-modulated radiotherapy. Phys Med Biol 2021; 66. [PMID: 34237715 DOI: 10.1088/1361-6560/ac127d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/08/2021] [Indexed: 11/12/2022]
Abstract
We try to develop an atlas-guided automatic planning (AGAP) approach and evaluate its feasibility and performance in rectal cancer intensity-modulated radiotherapy. The developed AGAP approach consisted of four independent modules: patient atlas, similar patient retrieval, beam morphing (BM), and plan fine-tuning (PFT) modules. The atlas was setup using anatomy and plan data from Pinnacle auto-planning (P-auto) plans. Given a new patient, the retrieval function searched the top similar patient by a generic Fourier descriptor algorithm and retrieved its plan information. The BM function generated an initial plan for the new patient by morphing the beam aperture from the top similar patient plan. The beam aperture and calculated dose of the initial plan were used to guide the new plan optimization in the PFT function. The AGAP approach was tested on 96 patients by the leave-one-out validation and plan quality was compared with the P-auto plans. The AGAP and P-auto plans had no statistical difference for target coverage and dose homogeneity in terms ofV100%(p = 0.76) and homogeneity index (p = 0.073), respectively. The CI index showed they had a statistically significant difference. But the ΔCI was both 0.02 compared to the perfect CI index of 1. The AGAP approach reduced the bladder mean dose by 152.1 cGy (p < 0.05) andV50by 0.9% (p < 0.05), and slightly increased the left and right femoral head mean dose by 70.1 cGy (p < 0.05) and 69.7 cGy (p < 0.05), respectively. This work developed an efficient and automatic approach that could fully automate the IMRT planning process in rectal cancer radiotherapy. It reduced the plan quality dependence on the planner experience and maintained the comparable plan quality with P-auto plans.
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Affiliation(s)
- Jiayuan Peng
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Yuanhua Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China.,Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Jun Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Xiang Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Bin Cai
- Department of Radiation Oncology, University of Texas Southwestern Medical Center Dallas, Texas 75390, United States of America
| | - Thomas R Mazur
- Department of Radiation Oncology, Washington University, St. Louis, MO 63110 United States of America
| | - Ji Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, People's Republic of China
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8
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Costa E, Richir T, Robilliard M, Bragard C, Logerot C, Kirova Y, Fourquet A, De Marzi L. Assessment of a conventional volumetric-modulated arc therapy knowledge-based planning model applied to the new Halcyon© O-ring linac in locoregional breast cancer radiotherapy. Phys Med 2021; 86:32-43. [PMID: 34051551 DOI: 10.1016/j.ejmp.2021.05.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/31/2021] [Accepted: 05/13/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION The aim of this study was to evaluate the performance of a knowledge-based planning (KBP) model for breast cancer trained on plans performed on a conventional linac with 6 MV FF (flattening filter) beams and volumetric-modulated arc therapy (VMAT) for plans performed on the new jawless Halcyon© system with 6 MV FFF (flattening filter-free) beams. MATERIALS AND METHODS Based on the RapidPlan© (RP) KBP optimization engine, a DVH Estimation Model was first trained using 56 VMAT left-sided breast cancer treatment plans performed on a conventional linac, and validated on another 20 similar cases (without manual intervention). To determine the capacity of the model for Halcyon©, an additional cohort of 20 left-sided breast cancer plans was generated with RP and analyzed for both TrueBeam© and Halcyon© machines. Plan qualities between manual vs RP (followed by manual intervention) Halcyon© plans set were compared qualitatively by blinded review by radiation oncologists for 10 new independent plans. RESULTS Halcyon© plans generated with the VMAT model trained with conventional linac plans showed comparable target dose distribution compared to TrueBeam© plans. Organ sparingwas comparable between the 2 devices with a slight decrease in heart dose for Halcyon© plans. Nine out of ten automatically generated Halcyon© plans were preferentially chosen by the radiation oncologists over the manually generated Halcyon© plans. CONCLUSION A VMAT KBP model driven by plans performed on a conventional linac with 6 MV FF beams provides high quality plans performed with 6 MV FFF beams on the new Halcyon© linac.
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Affiliation(s)
- Emilie Costa
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France.
| | - Thomas Richir
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Magalie Robilliard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christel Bragard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christelle Logerot
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Youlia Kirova
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Alain Fourquet
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Ludovic De Marzi
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France; Institut Curie, University Paris Saclay, PSL Research University, Inserm LITO, Orsay, France
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9
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Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med 2020; 14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022]
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
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Affiliation(s)
- Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
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10
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Ma M, K. Buyyounouski M, Vasudevan V, Xing L, Yang Y. Dose distribution prediction in isodose feature‐preserving voxelization domain using deep convolutional neural network. Med Phys 2019; 46:2978-2987. [DOI: 10.1002/mp.13618] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 05/01/2019] [Accepted: 05/02/2019] [Indexed: 01/30/2023] Open
Affiliation(s)
- Ming Ma
- Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847USA
| | - Mark K. Buyyounouski
- Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847USA
| | - Varun Vasudevan
- Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847USA
| | - Lei Xing
- Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847USA
| | - Yong Yang
- Department of Radiation Oncology Stanford University 875 Blake Wilbur Drive Stanford CA 94305‐5847USA
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11
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Ge Y, Wu QJ. Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches. Med Phys 2019; 46:2760-2775. [PMID: 30963580 PMCID: PMC6561807 DOI: 10.1002/mp.13526] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 01/15/2019] [Accepted: 03/26/2019] [Indexed: 12/20/2022] Open
Abstract
Purpose Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation. Conclusions The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology.
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Affiliation(s)
- Yaorong Ge
- Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA
| | - Q Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
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12
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Wall PD, Carver RL, Fontenot JD. Impact of database quality in knowledge-based treatment planning for prostate cancer. Pract Radiat Oncol 2018; 8:437-444. [DOI: 10.1016/j.prro.2018.03.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 01/31/2018] [Accepted: 03/17/2018] [Indexed: 12/25/2022]
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13
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Huijskens SC, van Dijk IWEM, Visser J, Balgobind BV, Te Lindert D, Rasch CRN, Alderliesten T, Bel A. Abdominal organ position variation in children during image-guided radiotherapy. Radiat Oncol 2018; 13:173. [PMID: 30208936 PMCID: PMC6136223 DOI: 10.1186/s13014-018-1108-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/20/2018] [Indexed: 12/03/2022] Open
Abstract
Background Interfractional organ position variation might differ for abdominal organs and this could have consequences for defining safety margins. Therefore, the purpose of this study is to quantify interfractional position variations of abdominal organs in children in order to investigate possible correlations between abdominal organs and determine whether position variation is location-dependent. Methods For 20 children (2.2–17.8 years), we retrospectively analyzed 113 CBCTs acquired during the treatment course, which were registered to the reference CT to assess interfractional position variation of the liver, spleen, kidneys, and both diaphragm domes. Organ position variation was assessed in three orthogonal directions and relative to the bony anatomy. Diaphragm dome position variation was assessed in the cranial-caudal (CC) direction only. We investigated possible correlations between position variations of the organs (Spearman’s correlation test, ρ), and tested if organ position variations in the CC direction are related to the diaphragm dome position variations (linear regression analysis, R2) (both tests: significance level p < 0.05). Differences of variations of systematic (∑) and random errors (σ) between organs were tested (Bonferroni significance level p < 0.004). Results In all directions, correlations between liver and spleen position variations, and between right and left kidney position variations were weak (ρ ≤ 0.43). In the CC direction, the position variations of the right and left diaphragm domes were significantly, and stronger, correlated with position variations of the liver (R2 = 0.55) and spleen (R2 = 0.63), respectively, compared to the right (R2 = 0.00) and left kidney (R2 = 0.25). Differences in ∑ and σ between all organs were small and insignificant. Conclusions No (strong) correlations between interfractional position variations of abdominal organs in children were observed. From present results, we concluded that diaphragm dome position variations could be more representative for superiorly located abdominal (liver, spleen) organ position variations than for inferiorly located (kidneys) organ position variations. Differences of systematic and random errors between abdominal organs were small, suggesting that for margin definitions, there was insufficient evidence of a dependence of organ position variation on anatomical location. Electronic supplementary material The online version of this article (10.1186/s13014-018-1108-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sophie C Huijskens
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
| | - Irma W E M van Dijk
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Jorrit Visser
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Brian V Balgobind
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - D Te Lindert
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Coen R N Rasch
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Tanja Alderliesten
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Arjan Bel
- Amsterdam UMC, University of Amsterdam, Department of Radiation Oncology, Cancer Center Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
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14
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Wang J, Chen Z, Li W, Qian W, Wang X, Hu W. A new strategy for volumetric-modulated arc therapy planning using AutoPlanning based multicriteria optimization for nasopharyngeal carcinoma. Radiat Oncol 2018; 13:94. [PMID: 29769101 PMCID: PMC5956620 DOI: 10.1186/s13014-018-1042-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 05/01/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A new strategy for making the appropriate choice of the representative optimization parameters in planning processes and accurate selection criteria during Pareto surface navigation for general multicriteria optimization (MCO) was recommended in the study. The purpose was to combine both benefits of AutoPlanning optimization and MCO (APMCO) for achieving an individual volumetric-modulated arc therapy (VMAT) plan according to the clinically achieved patient-specific tradeoff among conflicting priorities. The preclinical investigation of this optimization approach for nasopharyngeal carcinoma (NPC) radiotherapy was performed and compared to general MCO VMAT. METHODS A total of 60 NPC patients with various stages were enrolled in this study. General MCO and APMCO plans were generated for each patient on the treatment planning system. The differences between two planning schemes were evaluated and compared. RESULTS All plans were capable of achieving the prescription requirement. The planning target volume coverage and conformation number were remarkably similar between general MCO and APMCO plans. There were no significant differences in most of organs at risk (OARs) sparing. However, in APMCO plans, relatively remarkable decreases were observed in the mean dose (Dmean) to the glottic larynx and pharyngeal constrictor muscles. The reductions of average Dmean to the two OARs were 10.5% (p < 0.0001) and 8.4% (p < 0.0001), respectively. APMCO technique was found to increase the planning time for an average of approximately 5 h and did not lead to a significant increase of monitor units compared to general MCO. CONCLUSIONS The potential of the APMCO strategy is best realized with a clinical implementation that exploits individual generation of Pareto surface representations without manual interaction. It also assists physicians to ensure navigation in a more efficient and straightforward manner.
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Affiliation(s)
- Juanqi Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhi Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weiwei Li
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Qian
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaosheng Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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15
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Ahmed S, Nelms B, Gintz D, Caudell J, Zhang G, Moros EG, Feygelman V. A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning. Med Phys 2017; 44:5486-5497. [PMID: 28777469 DOI: 10.1002/mp.12500] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 12/16/2022] Open
Abstract
PURPOSE Despite improvements in optimization and automation algorithms, the quality of radiation treatment plans still varies dramatically. A tool that allows a priori estimation of the best possible sparing (Feasibility DVH, or FDVH) of an organ at risk (OAR) in high-energy photon planning may help reduce plan quality variability by deriving patient-specific OAR goals prior to optimization. Such a tool may be useful for (a) meaningfully evaluating patient-specific plan quality and (b) supplying best theoretically achievable DVH goals, thus pushing the solution toward automatic Pareto optimality. This work introduces such a tool and validates it for clinical Head and Neck (HN) datasets. METHODS To compute FDVH, first the targets are assigned uniform prescription doses, with no reference to any particular beam arrangement. A benchmark 3D dose built outside the targets is estimated using a series of energy-specific dose spread calculations reflecting observed properties of radiation distribution in media. For the patient, the calculation is performed on the heterogeneous dataset, taking into account the high- (penumbra driven) and low- (PDD and scatter-driven) gradient dose spreading. The former is driven mostly by target dose and surface shape, while the latter adds the dependence on target volume. This benchmark dose is used to produce the "best possible sparing" FDVH for an OAR, and based on it, progressively more easily achievable FDVH curves can be estimated. Validation was performed using test cylindrical geometries as well as 10 clinical HN datasets. For HN, VMAT plans were prepared with objectives of covering the primary and the secondary (bilateral elective neck) PTVs while addressing only one OAR at a time, with the goal of maximum sparing. The OARs were each parotid, the larynx, and the inferior pharyngeal constrictor. The difference in mean OAR doses was computed for the achieved vs. FDVHs, and the shapes of those DVHs were compared by means of the Dice similarity coefficient (DSC). RESULTS For all individually optimized HN OARs (N = 38), the average DSC between the planned DVHs and the FDVHs was 0.961 ± 0.018 (95% CI 0.955-0.967), with the corresponding average of mean OAR dose differences of 1.8 ± 5.8% (CI -0.1-3.6%). For realistic plans the achieved DVHs run no lower than the FDVHs, except when target coverage is compromised at the target/OAR interface. CONCLUSIONS For the validation of VMAT plans, the OAR DVHs optimized one-at-a-time were similar in shape to and bound on the low side by the FDVHs, within the confines of planner's ability to precisely cover the target(s) with the prescription dose(s). The method is best suited for the OARs close to the target. This approach is fundamentally different from "knowledge-based planning" because it is (a) independent of the treatment plan and prior experience, and (b) it approximates, from nearly first principles, the lowest possible boundary of the OAR DVH, but not necessarily its actual shape in the presence of competing OAR sparing and target dose homogeneity objectives.
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Affiliation(s)
- Saeed Ahmed
- Department of Physics, University of South Florida, Tampa, FL, 33612, USA
| | | | - Dawn Gintz
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Jimmy Caudell
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Geoffrey Zhang
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Eduardo G Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Vladimir Feygelman
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, 33612, USA
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16
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Wang J, Hu W, Yang Z, Chen X, Wu Z, Yu X, Guo X, Lu S, Li K, Yu G. Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients? Radiat Oncol 2017; 12:85. [PMID: 28532508 PMCID: PMC5440994 DOI: 10.1186/s13014-017-0822-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 05/15/2017] [Indexed: 12/25/2022] Open
Abstract
Background Knowledge-based planning (KBP) is a promising technique that can improve plan quality and increase planning efficiency. However, no attempts have been made to extend the domain of KBP for planners with different planning experiences so far. The purpose of this study was to quantify the potential gains for planners with different planning experiences after implementing KBP in intensity modulated radiation therapy (IMRT) plans for left-sided breast cancer patients. Methods The model libraries were populated with 80 expert clinical plans from treated patients who previously received left-sided breast-conserving surgery and IMRT with simultaneously integrated boost. The libraries were created on the RapidPlanTM. 6 planners with different planning experiences (2 beginner planners, 2 junior planners and 2 senior planners) generated manual and KBP optimized plans for additional 10 patients, similar to those included in the model libraries. The plan qualities were compared between manual and KBP plans. Results All plans were capable of achieving the prescription requirement. There were almost no statistically significant differences in terms of the planning target volume (PTV) coverage and dose conformality. It was demonstrated that the doses for most of organs-at-risk (OARs) were on average lower or equal in KBP plans compared to manual plans except for the senior planners, where the very small differences were not statistically significant. KBP data showed a systematic trend to have superior dose sparing at most parameters for the heart and ipsilateral lung. The observed decrease in the doses to these OARs could be achieved, particularly for the beginner and junior planners. Many differences were statistically significant. Conclusions It is feasible to generate acceptable IMRT plans after implementing KBP for left-sided breast cancer. KBP helps to effectively improve the quality of IMRT plans against the benchmark of manual plans for less experienced planners without any manual intervention. KBP showed promise for homogenizing the plan quality by transferring planning expertise from more experienced to less experienced planners.
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Affiliation(s)
- Juanqi Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Zhaozhi Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Xiaohui Chen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiqiang Wu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaoli Yu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaomao Guo
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Saiquan Lu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Kaixuan Li
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Gongyi Yu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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17
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Sung W, Kim JI, Kim HS, Kim HJ, Lee YH, Ye SJ. Performance of the irregular surface compensator compared with four-field box and intensity modulated radiation therapy for gynecologic cancer. Phys Med 2016; 32:1537-1542. [PMID: 27866896 DOI: 10.1016/j.ejmp.2016.10.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 10/28/2016] [Accepted: 10/31/2016] [Indexed: 10/20/2022] Open
Abstract
PURPOSE A retrospective planning study was undertaken to evaluate the dosimetric advantages of the irregular surface compensator (ISC) technique, a forward planning technique with electronic compensation algorithm available on Varian Eclipse treatment planning system. This was extensively compared to the conventional four-field box (4FB) and intensity modulated radiation therapy using 5 fields (IMRT5F) on gynecologic cancer patients. METHODS Twenty-two patients were enrolled. The prescribed dose was 50.4Gy in 28 fractions to the primary target including pelvic lymph nodes. 4FB treatment plans were generated, then fluence of anterior and posterior fields were modified to generate ISC plans. IMRT5F were inversely optimized with equally spaced five coplanar fields. Dose-volume parameters were evaluated for the comparison of three planning techniques. The MU and delivery time were also estimated. RESULTS In terms of target coverage, the conformity and homogeneity index of ISC (1.67 and 1.03, respectively) were superior to those of 4FB (2.43 and 1.06, respectively) but slightly inferior to those of IMRT5F (1.10 and 1.02, respectively). ISC also illustrated an overall improvement in normal organ saving. Compared to 4FB, the mean dose of the rectum was reduced by about 4.0-5.0Gy with ISC and IMRT5F. The volume receiving large doses was reduced for bladder with statistical significance with ISC and more with IMRT5F relative to 4FB. The mean number of MU per fraction were 200.86 (4FB), 446.09 (ISC) and 895.59 (IMRT5F). CONCLUSION The ISC technique has the superior target coverage and healthy tissue sparing in comparison with conventional 4FB and comparable normal organ saving compared to IMRT5F. The ISC can be an available option for gynecologic radiotherapy.
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Affiliation(s)
- Wonmo Sung
- Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Seoul National University Graduate School of Convergence Science and Technology, Seoul, Republic of Korea; Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung-In Kim
- Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea; Center for Convergence Research on Robotics, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea.
| | - Hyun Suk Kim
- Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Seoul National University Graduate School of Convergence Science and Technology, Seoul, Republic of Korea; Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hak Jae Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Yang Hoon Lee
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung-Joon Ye
- Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Seoul National University Graduate School of Convergence Science and Technology, Seoul, Republic of Korea; Biomedical Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
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